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Related Experiment Video

Updated: Jul 18, 2025

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Published on: December 21, 2019

Dryland sensitivity to climate change and variability using nonlinear dynamics.

Takehiro Sasaki¹, Scott L Collins², Jennifer A Rudgers²

¹Graduate School of Environment and Information Sciences, Yokohama National University, Hodogaya, Yokohama 240-8501, Japan.

Proceedings of the National Academy of Sciences of the United States of America

|August 21, 2023

Summary

This summary is machine-generated.

Grassland productivity shows complex responses to climate change, varying by region and time. Understanding these nonlinear, state-dependent relationships is crucial for predicting ecosystem feedback to climate.

Keywords:

aridification climate extreme convergent cross-mapping drought legacy empirical dynamic modeling

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Area of Science:

Ecology
Climate Science
Time-Series Analysis

Background:

Primary productivity's response to climate is temporally variable, suggesting state-dependent interactions.
Previous research often used equation-based models, overlooking ecological dynamics' nonlinear nature.

Purpose of the Study:

To reveal sensitivity patterns of grassland productivity to climate change and variability using an equation-free approach.
To clarify the underlying mechanisms driving these state-dependent productivity responses.

Main Methods:

Analysis of 40 years of climate and productivity data from 48 Mongolian grassland sites.
Application of nonlinear time-series analysis, avoiding equation-based models.

Main Results:

Productivity responded positively to precipitation in mesic regions and negatively in arid regions.
Temperature showed opposite patterns, while decreasing aridity negatively impacted productivity.
Interannual variability responses differed by region, with temperature variability generally enhancing productivity.

Conclusions:

Highlighting the importance of nonlinear, state-dependent sensitivity of productivity to climate change.
Identifying time-delayed climate effects and species' stress resistance as key mechanisms.
Emphasizing the need for accurate forecasting of biosphere feedback to the climate system.